scholarly journals RichDEM: High-performance terrain analysis

2018 ◽  
Author(s):  
Richard Barnes
Keyword(s):  
High Performance ◽  
Landscape Evolution ◽  
Curvature Flow ◽  
Terrain Analysis ◽  
Spatial And Temporal Scales ◽  
Flow Accumulation ◽  
Terrain Attributes ◽  
Core Set ◽  
Do So

To answer geomorphological questions at unprecedented spatial and temporal scales, we need to (a) parse terabyte-scale datasets (DEMs), (b) perform millions of model realizations to pinpoint the parameters which govern landscape evolution, and (c) do so with statistical rigor, which may require thousands of additional realizations. A core set of operations underpin many geomorphic models. These include determination of terrain attributes such as slope and curvature; flow routing; depression flooding and breaching; flat resolution; and flow accumulation. Here, I present RichDEM, a high-performance C++ library and set of wrappers for performing these operations. The library incorporates a number of options for performing each operation and makes full use of modern high-performance capabilities. The library can scale to process DEMs of over one trillion cells and operates effectively on laptops or supercomputers.

scholarly journals RichDEM: High-performance terrain analysis

2018 ◽  
Author(s):  
Richard Barnes
Keyword(s):  
High Performance ◽  
Landscape Evolution ◽  
Curvature Flow ◽  
Terrain Analysis ◽  
Spatial And Temporal Scales ◽  
Flow Accumulation ◽  
Terrain Attributes ◽  
Core Set ◽  
Do So

To answer geomorphological questions at unprecedented spatial and temporal scales, we need to (a) parse terabyte-scale datasets (DEMs), (b) perform millions of model realizations to pinpoint the parameters which govern landscape evolution, and (c) do so with statistical rigor, which may require thousands of additional realizations. A core set of operations underpin many geomorphic models. These include determination of terrain attributes such as slope and curvature; flow routing; depression flooding and breaching; flat resolution; and flow accumulation. Here, I present RichDEM, a high-performance C++ library and set of wrappers for performing these operations. The library incorporates a number of options for performing each operation and makes full use of modern high-performance capabilities. The library can scale to process DEMs of over one trillion cells and operates effectively on laptops or supercomputers.

scholarly journals Terrain Analysis in Google Earth Engine: A Method Adapted for High-Performance Global-Scale Analysis

2020 ◽  
Vol 9 (6) ◽  
pp. 400 ◽  
Author(s):  
José Safanelli ◽  
Raul Poppiel ◽  
Luis Ruiz ◽  
Benito Bonfatti ◽  
Fellipe Mello ◽  
...  
Keyword(s):  
High Performance ◽  
Global Scale ◽  
Google Earth ◽  
Slope Aspect ◽  
Analysis Tool ◽  
Terrain Analysis ◽  
Terrain Attributes ◽  
Visual Correspondence ◽  
Elevation Data ◽  
Google Earth Engine

Terrain analysis is an important tool for modeling environmental systems. Aiming to use the cloud-based computing capabilities of Google Earth Engine (GEE), we customized an algorithm for calculating terrain attributes, such as slope, aspect, and curvatures, for different resolution and geographical extents. The calculation method is based on geometry and elevation values estimated within a 3 × 3 spheroidal window, and it does not rely on projected elevation data. Thus, partial derivatives of terrain are calculated considering the great circle distances of reference nodes of the topographic surface. The algorithm was developed using the JavaScript programming interface of the online code editor of GEE and can be loaded as a custom package. The algorithm also provides an additional feature for making the visualization of terrain maps with a dynamic legend scale, which is useful for mapping different extents: from local to global. We compared the consistency of the proposed method with an available but limited terrain analysis tool of GEE, which resulted in a correlation of 0.89 and 0.96 for aspect and slope over a near-global scale, respectively. In addition to this, we compared the slope, aspect, horizontal, and vertical curvature of a reference site (Mount Ararat) to their equivalent attributes estimated on the System for Automated Geospatial Analysis (SAGA), which achieved a correlation between 0.96 and 0.98. The visual correspondence of TAGEE and SAGA confirms its potential for terrain analysis. The proposed algorithm can be useful for making terrain analysis scalable and adapted to customized needs, benefiting from the high-performance interface of GEE.

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